Photon Counting Versus Energy-integrated Detector CT in Detection of Superior Semicircular Canal Dehiscence.

BACKGROUND: Superior semicircular canal dehiscence (SSCD), an osseous defect overlying the SSC, is associated with a constellation of audiovestibular symptoms. This study sought to compare conventional energy-integrated detector (EID) computed tomography (CT) to photon-counting detector (PCD)-CT in the detection of SSCD. MATERIAL AND METHODS: Included patients were prospectively recruited to undergo a temporal bone CT on both EID-CT and PCD-CT scanners. Two blinded neuroradiologists reviewed both sets of images for 1) the presence or absence of SSCD (graded as present, absent, or indeterminate), and 2) the width of the bone overlying the SSC (if present). Any discrepancies in the presence or absence of SSCD were agreed upon by consensus. RESULTS: In the study 31 patients were evaluated, for a total of 60 individual temporal bones (2 were excluded). Regarding SSCD presence or absence, there was substantial agreement between EID-CT and PCD-CT (k = 0.76; 95% confidence interval, CI 0.54-0.97); however, SSCD was present in only 9 (15.0%) temporal bones on PCD-CT, while EID-CT examinations were interpreted as being positive in 14 (23.3%) temporal bones. This yielded a false positive rate of 8.3% on EID-CT. The bone overlying the SSC was thinner on EID-CT images (0.66 mm; SD = 0.64) than on PCD-CT images (0.72 mm; SD = 0.66) (p < 0.001). CONCLUSION: The EID-CT examinations tend to overcall the presence of SSCD compared to PCD-CT and also underestimate the thickness of bone overlying the SSC.

PleThora: Pleural effusion and thoracic cavity segmentations in diseased lungs for benchmarking chest CT processing pipelines.

This manuscript describes a dataset of thoracic cavity segmentations and discrete pleural effusion segmentations we have annotated on 402 computed tomography (CT) scans acquired from patients with non-small cell lung cancer. The segmentation of these anatomic regions precedes fundamental tasks in image analysis pipelines such as lung structure segmentation, lesion detection, and radiomics feature extraction. Bilateral thoracic cavity volumes and pleural effusion volumes were manually segmented on CT scans acquired from The Cancer Imaging Archive "NSCLC Radiomics" data collection. Four hundred and two thoracic segmentations were first generated automatically by a U-Net based algorithm trained on chest CTs without cancer, manually corrected by a medical student to include the complete thoracic cavity (normal, pathologic, and atelectatic lung parenchyma, lung hilum, pleural effusion, fibrosis, nodules, tumor, and other anatomic anomalies), and revised by a radiation oncologist or a radiologist. Seventy-eight pleural effusions were manually segmented by a medical student and revised by a radiologist or radiation oncologist. Interobserver agreement between the radiation oncologist and radiologist corrections was acceptable. All expert-vetted segmentations are publicly available in NIfTI format through The Cancer Imaging Archive at https://doi.org/10.7937/tcia.2020.6c7y-gq39. Tabular data detailing clinical and technical metadata linked to segmentation cases are also available. Thoracic cavity segmentations will be valuable for developing image analysis pipelines on pathologic lungs - where current automated algorithms struggle most. In conjunction with gross tumor volume segmentations already available from "NSCLC Radiomics," pleural effusion segmentations may be valuable for investigating radiomics profile differences between effusion and primary tumor or training algorithms to discriminate between them.